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PubMed2026

Shared genetic architecture between ADHD and intelligence varies across ADHD subtypes.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental condition frequently accompanied by cognitive difficulties. Although previous genetic studies have demonstrated substantial overlap between ADHD and intelligence, most have treated ADHD as a single phenotype. However, whether this shared genetic architecture differs across ADHD subtypes remains unclear. METHODS: We conducted a genome-wide cross-trait analysis integrating large-scale genome-wide association study (GWAS) datasets of overall ADHD, its subtypes-childhood ADHD, persistent ADHD, and late-diagnosed ADHD-and intelligence (total N > 300,000). Genome-wide genetic correlations, polygenic overlap, local genetic correlations, and variant-level associations between ADHD phenotypes and intelligence were evaluated to characterize their shared genetic architecture. Shared variants were identified through cross-trait enrichment analyses and subsequently mapped to genes for functional annotation and gene-set enrichment. Bidirectional associations were evaluated using two-sample Mendelian randomization with sensitivity analyses. Additional GWAS datasets were used to validate the robustness of shared loci by assessing the consistency of effect directions. RESULTS: All ADHD phenotypes showed significant negative genetic correlations with intelligence (rg ranging from -0.3442 to -0.4205). Despite these modest genome-wide correlations, cross-trait analyses revealed substantial genetic overlap, including polygenic overlap, local genetic correlations, and variant-level associations. We identified 184 loci jointly associated with ADHD traits and intelligence, including 64 novel loci, whereas no shared loci were detected for persistent ADHD under the current analysis. Functional annotation revealed biologically distinct enrichment patterns across subtypes: childhood ADHD loci were linked to early neurodevelopmental processes, while late-diagnosed ADHD loci were enriched in synapse-related and neuronal signaling pathways. Mendelian randomization analyses suggested bidirectional associations, with stronger evidence supporting a directional association from intelligence to ADHD risk. Furthermore, these shared loci showed largely consistent effect directions across additional GWAS datasets, providing support for the robustness of the findings. CONCLUSIONS: The shared genetic architecture between ADHD and intelligence varies across ADHD subtypes, highlighting distinct biological pathways underlying cognitive heterogeneity in ADHD. These findings suggest that the relationship between ADHD liability and general cognitive ability is not uniform across ADHD subtypes and may inform future research on risk stratification and early identification in child and adolescent psychiatry.

Humans↗
PubMed2026

Automated Classification of Lymphoma Subtypes From Histopathological Images Using a U-Net Deep Learning Model: Comparative Evaluation Study.

BACKGROUND: Accurate classification and grading of lymphoma subtypes are essential for treatment planning. Traditional diagnostic methods face challenges of subjectivity and inefficiency, highlighting the need for automated solutions based on deep learning techniques. OBJECTIVE: This study aimed to investigate the application of deep learning technology, specifically the U-Net model, in classifying and grading lymphoma subtypes to enhance diagnostic precision and efficiency. METHODS: In this study, the U-Net model was used as the primary tool for image segmentation integrated with attention mechanisms and residual networks for feature extraction and classification. A total of 620 high-quality histopathological images representing 3 major lymphoma subtypes were collected from The Cancer Genome Atlas and the Cancer Imaging Archive. All images underwent standardized preprocessing, including Gaussian filtering for noise reduction, histogram equalization, and normalization. Data augmentation techniques such as rotation, flipping, and scaling were applied to improve the model's generalization capability. The dataset was divided into training (70%), validation (15%), and test (15%) subsets. Five-fold cross-validation was used to assess model robustness. Performance was benchmarked against mainstream convolutional neural network architectures, including fully convolutional network, SegNet, and DeepLabv3+. RESULTS: The U-Net model achieved high segmentation accuracy, effectively delineating lesion regions and improving the quality of input for classification and grading. The incorporation of attention mechanisms further improved the model's ability to extract key features, whereas the residual structure of the residual network enhanced classification accuracy for complex images. In the test set (N=1250), the proposed fusion model achieved an accuracy of 92% (1150/1250), a sensitivity of 91.04% (1138/1250), a specificity of 89.04% (1113/1250), and an F1-score of 90% (1125/1250) for the classification of the 3 lymphoma subtypes, with an area under the receiver operating characteristic curve of 0.95 (95% CI 0.93-0.97). The high sensitivity and specificity of the model indicate strong clinical applicability, particularly as an assistive diagnostic tool. CONCLUSIONS: Deep learning techniques based on the U-Net architecture offer considerable advantages in the automated classification and grading of lymphoma subtypes. The proposed model significantly improved diagnostic accuracy and accelerated pathological evaluation, providing efficient and precise support for clinical decision-making. Future work may focus on enhancing model robustness through integration with advanced algorithms and validating performance across multicenter clinical datasets. The model also holds promise for deployment in digital pathology platforms and artificial intelligence-assisted diagnostic workflows, improving screening efficiency and promoting consistency in pathological classification.

Humans↗
PubMed2026

Unveiling novel transcriptomic prognostic biomarkers for specific breast cancer subtypes and treatment regimens.

BACKGROUND: Breast cancer (BRCA) is the most common cancer in women worldwide, yet current gene expression panels offer limited insight into treatment responses across different subtypes and therapies. This study aimed to identify reliable biomarkers for predicting treatment outcomes in specific BRCA subtypes and treatment regimens. METHODS: This study analyzed transcriptomic data from The Cancer Genome Atlas to identify differentially expressed genes (DEGs) in patient groups treated with different combinations of hormone therapy (H), chemotherapy (C), radiotherapy (R), and targeted therapy (T). Non-negative matrix factorization clustering was performed to stratify patients into clusters representing different BRCA subtypes. Functional enrichment analysis was performed, and survival assessments were conducted using the METABRIC dataset. RESULTS: A total of 1,148 DEGs were identified across treatment regimens, with 75 common DEGs shared across multiple regimens. Among these, 12 candidate biomarkers were associated with luminal subtypes treated with H, including LRP1B, of which high expression predicted cancer recurrence. In triple-negative breast cancer (TNBC) treated with C, 76 candidate biomarkers were identified, including TTYH1 for recurrence and ANXA8L1 and MPZ for non-recurrence. Functional analyses identified intermediate filament organization and keratinization as pathways associated with specific candidate biomarkers of TNBC following C. Survival analysis using METABRIC strengthened the prognostic ability of LRP1B and TTYH1 to predict worse survival and ANXA8L1 and MPZ to predict prolonged survival, with four additional prognostic biomarkers. CONCLUSION: This study identified gene expression prognostic biomarkers for luminal and TNBC subtypes, thereby supporting personalized therapies. Further experimental validation is required to confirm these findings for clinical application. CLINICAL TRIAL REGISTRY: No.

Breast cancer↗
PubMed2026

Whole genome sequencing of unusual Hepatitis C virus subtypes and drug resistance analysis during direct-acting antiviral therapy in India.

INTRODUCTION AND OBJECTIVES: Pangenotypic direct-acting antivirals (DAA) are effective against highly prevalent Hepatitis C virus (HCV) subtypes, but have been clinically validated almost exclusively in high-income countries. Unusual HCV subtypes may carry natural polymorphisms, potentially impacting DAA susceptibility. We conducted full-genome characterization and resistance analysis of unusual HCV subtypes in patients receiving DAA treatment. PATIENTS AND METHODS: In this prospective hospital-based study, eligible patients were screened for anti-HCV antibodies and active infection was confirmed by diagnostic 5'NCR-based HCV RNA detection. Genotyping was performed by core region sequencing, and viral load quantified by real-time PCR. For whole genome sequencing, multiplex primers were designed using alignments of global reference sequences. Sequencing was carried out using the Oxford Nanopore Technology platform. Phylogenetic analysis used multiple sequence alignment and the HCV-GLUE resource for resistance-associated substitution (RAS) analysis. RESULTS: Predominant genotype was genotype 3 in 64.3% (n = 45); genotype 6 in 21.4% (n = 15); and genotype 1 in 14.2% (n = 10). Unusual HCV subtype 6xa was detected in two patients and showed no NS5A resistance mutations. One genotype 3b patient relapsed at 24 weeks post-DAA treatment completion and carried NS5A resistance-associated substitutions 30 K and 31 M both at baseline and at relapse, conferring high-level resistance to NS5A inhibitors. CONCLUSION: This is the first report from India of whole genome sequencing of HCV subtype 6xa. The identification of NS5A resistance mutations in the 3b relapse case underscores challenges for global HCV elimination strategies.

Humans↗
PubMed2026

Evolution and heterogeneity of lethal metastatic bladder cancer subtypes.

Histological variation is a prognostic feature of metastatic urothelial cancer1-3, but its evolutionary trajectory remains poorly defined. We developed a metastatic bladder cancer rapid autopsy programme enriched in histological subtypes4 to profile individuals with terminal disease. Here by reconstructing the evolutionary histories of patient tumours, we show that metastasis-to-metastasis seeding is the dominant pattern of cancer spread and that increased polyclonal migration predicts poor prognosis. The burden, heterogeneity and timing of genomic alterations differ markedly among histological subtypes. Plasmacytoid and neuroendocrine variants develop early driver alterations associated with shorter survival. Mutational signature analyses and experimental models demonstrated that plasmacytoid tumours uniquely use the Fanconi anaemia pathway to mitigate chemotherapy-induced genomic scarring. Single-nucleus profiling revealed mixed cell states in histological subtypes and an association between transcriptional heterogeneity and patient survival. Characterization of the tumour microenvironment uncovered distinct immune states across subtypes, with plasmacytoid tumours exhibiting immune-inflamed profiles, whereas squamous tumours are predominantly immunosuppressive. Last, we demonstrate that post-mortem cell-free DNA captures genomic and transcriptional heterogeneity of the subtypes, which provides a potential strategy for noninvasive assessment of tumour identity and aggressiveness. Our results provide new insights into how tumour heterogeneity shapes the evolutionary history of disease progression in bladder cancer histological subtypes.

Journal Article↗
PubMed1979

Simultaneous occurrence in the same serum of hepatitis B surface antigen and antibody to hepatitis B surface antigen of different subtypes.

The simultaneous occurrence of hepatitis B surface antigen (HBsAg) and antibody to HBsAg (anti-HBs) of different subtypes in the serum of a hemodiaylzed patient was studied. The w(a) subdeterminants were involved. The HBsAg belonged to the ayw3 subtype, and the anti-HBs exhibited monospecific anti-w2 activity. Both the HBsAg and the anti-HBs were detectable by counterelectrophoresis (CEP). The specificity of the antibody was demonstrated by CEP in tests against 128 sera containing HBsAg of 12 different subtypes and in absorption experiments with eight sera containing HBsAg of eight different subtypes, as well as by radioimmunoassay in the liquid phase. The monospecific antibody was selectively directed against the w2 subdeterminant of the adw2 subtype and was designated anti-w2.

Absorption↗
PubMed1978

Choosing control groups in the study of schizophrenic subtypes.

The choice of comparison groups in the study of schizophrenic subtypes is discussed. Current research rests upon the comparison of schizophrenic subtypes and controls unselected for the subtype characteristics used to divide the schizophrenics. One cannot infer, on the basis of this type of group comparison, whether subtype differences are specific to schizophrenia or reflect general personality and behavioral characteristics. It is suggested, therefore, that control groups be selected for their comparability to the schizophrenics on subtype characteristics.

Attention↗
PubMed1979

An attempted integration of information relevant to schizophrenic subtypes.

The usefulness and validity of traditional subtypes are questionable. The subtypes described in earlier years no longer emerge with the clarity previously described. The four classical subtypes cannot be reliably distinguished and have not been shown to have predictive validity. Subtypes classified along course or prognostic lines may be more clinically useful. Attempts to subdivide schizophrenia along biologic and genetic lines offer promise. Recent efforts to describe new subdivisions of schizophrenia are readily justified, but new descriptive subtypes are likely to prove useful only when validated by biological, genetic, treatment response, and outcome data.

Adult↗
PubMed1979

Epilogue: subtypes of the schizophrenic syndrome--their current status.

The expectation that important subtypes exist in dementia praecox (schizophrenia) was built into the earliest conceptualizations of this disorder by Kraepelin and Bleuler. Although the traditional subtypes are still used, more recent biological, psychological, and descriptive-clinical data suggest that quite different approaches to subtyping and subtyping categories may be more valid. No definitive answers to the subtyping problem have yet been reached, but the solution may well involve a complex consideration of biologic, psychologic, and social variables.

Diagnosis, Differential↗
PubMedDate not supplied

Hepatitis B antigen subtypes-history, significance and immunogenicity.

Initial work showed that all hepatitis B surface antigens (HBs Ag) were identical. Subsequent, additional studies revealed that hepatitis B surface antigens have a group reactive determinant, a, plus additional specificities which are not universally present on all antigens. Four well-defindd subtypes of HBs Ag exist (HBs Ag/adw, HBs Ag/ayw, HBs Ag/adr, HBs Ag/ayr) but additional subtypes will be forthcoming as newly described determinants are confirmed. The subtype specificities are determined by the hepatitis B virus and not the host. Subtypes of HBs Ag are already of great use in the epidemiology of hepatitis B virus infections; yet they may have additional significance. Current work on HBs Ag subtypes is limited by the ability to find or produce antibodies to other than the a or group reactive determinant. The other determinants appear to be less immunogenic than a.

Animals↗
PubMed2026

Metabolism pathway-based subtyping in pancreatic adenocarcinoma: an integrated study by bulk RNA-sequence and machine learning algorithms.

BACKGROUND: Pancreatic adenocarcinoma (PAAD) is highly aggressive, and its tumor microenvironment has significant metabolic and immune microenvironment complexity and genomic instability. In this study, by integrating the metabolic pathway activity score and clinical data, we constructed a novel risk assessment model to reveal the unique biological behavior and clinical significance behind different PAAD subtypes. METHODS: In this study, the transcriptome and clinical data of TCGA and GSE57495 databases were integrated to explore the interaction between metabolic pathways. Based on unsupervised clustering analysis of pathway activity and survival prognosis, patients with PAAD were classified into metabolic subtypes with significant prognostic differences. Subsequently, we assessed the heterogeneity of these subtypes in terms of clinical outcomes, genomic characteristics, and immune microenvironment composition. Based on the differentially expressed genes (DEGs) among metabolic subtypes, a clinical prognostic risk model and nomogram were constructed, which were double-validated by GSE57495-independent cohort and GSE57495 + TCGA-PAAD combined cohort. Finally, the correlations between risk scores (RSs) and signaling pathway activity and tumor immune microenvironment characteristics were evaluated. RESULTS: Based on metabolic pathway correlation and prognostic information, 240 patients in the TCGA-PAAD and GSE57495 datasets were divided into three subgroups. There were significant differences between subgroups in gene expression, pathway activity, clinical prognosis, and immune infiltration characteristics among the subtypes. Using machine learning algorithms, an RS model was constructed from DEGs among the subgroups, with the random forest method showing the best performance. A nomogram integrating the RS and clinical indicators demonstrated excellent predictive accuracy for 1-, 3-, and 5-year survival rates, confirming the RS as an independent prognostic factor. High- and low-risk groups exhibited significant differences in immune infiltration, pathway activity, and gene mutations. Drug sensitivity analysis showed that the high-risk group was more sensitive to AZD6244, ABT737, and other drugs. CONCLUSION: This study stratified patients with PAAD into three subgroups based on metabolic pathways and prognostic information, revealing significant differences in clinical outcomes, immune characteristics, and genetic mutations. The robust RS model developed from these findings demonstrated strong predictive power for patient survival and identified promising therapeutic strategies, providing valuable insights for advancing precision medicine in PAAD.

immune microenvironment↗
PubMed2025

Integrative proteomic analysis provides novel therapeutic insights for etiological subtypes of diabetes.

AIMS: Type 2 diabetes (T2D) is a highly heterogeneous disease characterised by subtypes with variations in aetiology, disease progression, and risk of complications. However, potential drug targets for these subtypes have not been explored. This study aims to investigate potential drug targets by integrating proteomics. MATERIALS AND METHODS: Summary-level data of circulating proteins were extracted from the UK Biobank and the deCODE Health Study. Genetic associations with five diabetes subtypes were obtained from Swedish All New Diabetics in Scania and Malmö Diet and Cancer cohort, including severe autoimmune diabetes (SAID), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD). The associations between circulating proteins and diabetes subtypes were assessed through Mendelian randomisation, followed by multiple sensitivity and colocalization analyses. Additionally, tissue-specific, pathway and functional enrichment analysis, assessment of protein druggability, and the protein-protein interaction (PPI) networks were used to further explore biological mechanisms and therapeutic potential. RESULTS: Genetically predicted levels of 2, 2, 9, 3, and 5 circulating proteins were associated with SIRD, SIDD, MARD, MOD, and SAID, respectively. Colocalization analyses further revealed links between GRN with MARD/SIRD, LILRB5 with SIDD/MARD, CR1 with MARD, TNFSF12 with MOD, and DAPK2 with SAID. Enrichment analysis suggested that these proteins were mainly enriched in blood and adipose tissues and involved in immune and inflammatory related pathways. PPI analysis revealed GRN, TNFSF12, and DAPK2 are associated with known T2D targets. CONCLUSIONS: Our study identified several potential drug targets for different subtypes of diabetes using an integrated genetic approach, yielding new insights for precision medicine of diabetes.

Humans↗
PubMed1978

New hepatitis B surface antigen subtypes inside the ad category.

In addition to the 10 HBs Ag subtypes already described, 2 new subtypes were defined by using the q determinant. Exceptions to the rule generally accepted were found in that the q determinant was only lacking in HBs Ag/adw4. These exceptions occurred in adw and adr categories. These 2 new subtypes are adw q positive and adr q negative. Out of 98 HBs Ag/adw4 from silent carries and patients from different parts of the world, mainly from France (79), 6 were found q positive. 3 out of these 6 cases came from Montpellier (South of France) and another 3 from Germany. The 92 other cases were found q negative. Further studies will be necessary to better know the location of this new subtype adw4 q positive, but it seems to be present only in certain parts of Europe. Out of 86 HBs Ag/adr from silent carriers from Asia (58), Oceania (17), France (10, most of them contaminated in Asia) and Réunion (1), 10 were found q negative. All these 10 cases were detected in Oceania, 2 out of 2 in carriers from New Caledonia and 8 out of 13 from French Polynesia. The new subtype adr q negative seems localized in Melanesia and Polynesia and absent from Asia. These 2 new markers of hepatitis B virus will allow better epidemiological and geographical studies.

Epitopes↗
PubMed2026

Large-Scale Proteomic Profiling of Incident Heart Failure and Its Subtypes in Older Adults.

BACKGROUND: Heart failure (HF) and its main subtypes, heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF), impose an enormous health burden on elders. Assessment of the circulating proteome to illuminate pathogenesis could open new opportunities for treatment. METHODS: We conducted a plasma proteomics screen of incident HF and its subtypes in 2 older population-based cohorts, the CHS (Cardiovascular Health Study) and the AGES-RS (Aging, Gene/Environment Susceptibility-Reykjavik Study). The 2 studies used SomaLogic platforms, with 4404 aptamers in common. Multivariable Cox models were fit to evaluate individual-protein associations with HF, HFpEF, and HFrEF separately in each cohort, and study-specific associations were combined by fixed-effects meta-analysis. Replication was performed in the ARIC (Atherosclerosis Risk in Communities) cohort. Two-sample Mendelian randomization of HF and its subtypes, along with colocalization analysis, was performed to support causal inference. RESULTS: Among 8599 participants, 1590 experienced incident HF (536 HFpEF, 471 HFrEF). There were 119 proteins associated with HF, 15 proteins with HFpEF, and 11 proteins with HFrEF, at Bonferroni-corrected significance. Among these, 9 have never previously been identified for cardiovascular diseases, and another 61 represent new associations with incident HF or its subtypes. Of these 70 proteins, 55 of the 66 available replicated externally. Mendelian randomization analysis revealed 7 proteins genetically associated with HF at nominal significance; 2 were separately associated with HFpEF, and another 2 with HFrEF. Seven of these 9 proteins (NPDC1 [neural proliferation differentiation and control protein 1], APOF [apolipoprotein F], LMAN2 [lectin, mannose-binding 2], ADIPOQ [adiponectin], CD14 [cluster of differentiation 14], ARHGAP1 [Rho GTPase-activating protein 1], C9 [complement 9]) showed new, possibly causal associations, although we did not detect evidence for colocalization. CONCLUSIONS: In this large-scale proteomic study involving 3 longitudinal cohorts of older adults, we identified and replicated 55 novel protein markers of HF or its subtypes, and 7 new, possibly causal proteins. These proteins may enhance risk prediction, improve understanding of pathobiology, and help prioritize targets for therapeutic development of these foremost disorders in elders.

Humans↗
PubMed2026

Dissecting the shared genetic architecture between migraine subtypes and cardiovascular diseases: a multi-layered genomic analysis.

BACKGROUND: Epidemiological studies have linked migraine to an increased risk of cardiovascular disease (CVD); however, the shared genetic basis and putative causal relationships between migraine subtypes and cardiovascular traits remain poorly understood. METHODS: Leveraging large-scale GWAS summary statistics for migraine phenotypes (overall migraine, migraine with aura [MA], and migraine without aura [MO]) from FinnGen R12, along with seven cardiovascular diseases from publicly available consortia, we conducted a multi-layered genetic analysis. This integrative framework encompassed genetic correlation [linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL)], cross-trait meta-analysis (CPASSOC and PLACO), Bayesian colocalization, summary-data-based Mendelian randomization (SMR) using GTEx v8 eQTL data, and bidirectional two-sample Mendelian randomization (MR). RESULTS: Significant genetic correlations were identified between migraine and multiple cardiovascular traits, with hypertension and coronary artery disease (CAD) showing the most robust associations. MA exhibited broader genetic overlap with cardiovascular diseases than MO, including a notably stronger correlation with ischemic stroke, whereas MO demonstrated a stronger correlation with hypertension. Cross-trait meta-analysis identified 160 pleiotropic loci across 17 of 21 trait pairs. Colocalization analysis confirmed 32 loci harboring shared causal variants, mapped to 13 candidate genes, of which 7 (PHACTR1, LRP1, SOX7, ABO, FHOD3, MEI1, XKR6) were further validated by SMR as exhibiting tissue-specific regulatory effects. Among these, PHACTR1 displayed the broadest pleiotropic profile across migraine phenotypes and vascular diseases. After MR-PRESSO outlier removal, bidirectional MR identified 10 MR-supported associations, two of which (genetic liability to hypertension on overall migraine, and CAD on MA) survived Bonferroni correction, all free of detectable horizontal pleiotropy. Genetic liability to hypertension was associated with increased migraine risk (OR = 1.90, 95% CI 1.25-2.90, P = 2.64 × 10⁻³), atherosclerotic diseases showed subtype-specific effects (inverse for MO, positive for MA), and, in the reverse direction, migraine was associated with increased ischemic stroke risk. CONCLUSIONS: This study provides a comprehensive and systematic characterization of the shared genetic architecture between migraine subtypes and cardiovascular diseases. By identifying pleiotropic genes and bidirectional putative causal relationships with subtype-specific patterns, our findings carry implications for the development of targeted therapeutics and subtype-specific cardiovascular risk stratification.

Humans↗
PubMed2025

Proteomic-based biomarker discovery reveals panels of diagnostic biomarkers for early identification of heart failure subtypes.

BACKGROUND: Limited access to echocardiography can delay the diagnosis of suspected heart failure (HF), which in turn postpones the initiation of optimal guideline-directed medical therapy. Although natriuretic peptides like B-type natriuretic peptide (BNP) are valuable biomarkers for diagnosing and managing HF, the utility of combining BNP with other blood-based biomarkers to predict subtypes of new-onset HF remains underexplored. OBJECTIVES: This study sought to investigate and evaluate the diagnostic significance of adding blood-based biomarkers to BNP for identifying heart failure with preserved ejection fraction (HFpEF) or reduced ejection fraction (HFrEF), with the goal of enhancing diagnostic assays beyond BNP measurements. METHODS: We identified candidate blood protein biomarkers using untargeted proteomics workflows from a cohort of individuals recruited to the STOP-HF trial who were at risk of HF and subsequently developed either HFpEF or HFrEF over time ("HF progressors"; n = 40). Candidate biomarkers were verified in an independent cohort (n = 52) from a community-based rapid access HF diagnostic clinic. The biological processes associated with these proteins were assessed, and the diagnostic values of biomarker panels were evaluated using a machine learning approach. RESULTS: Within HF progressors, we identified 3 proteins associated with HFpEF development: vascular cell adhesion protein 1 (VCAM1), insulin-like growth factor 2 (IGF2), and inter-alpha-trypsin inhibitor heavy chain 3 (ITIH3). Additionally, 4 proteins were linked to HFrEF development: C-reactive protein (CRP), interleukin-6 receptor subunit beta (IL6RB), phosphatidylinositol-glycan-specific phospholipase D (PHLD), and noelin (NOE1). These findings were verified in an independent cohort to distinguish HF subtypes from controls. Moreover, a random forest algorithm demonstrated that combining these candidate biomarkers with BNP measurement significantly improved the prediction of HF subtypes. CONCLUSIONS: We identified candidate proteins linked to HFpEF and HFrEF in a longitudinal HF progressor cohort and validated them in a community-based cohort. Adding these proteins to BNP led to a significant improvement in HF subtype prediction. Study results have clinical implications for blood-based screening of HF subtypes using panels of biomarkers, particularly in resource-limited settings.

Humans↗
PubMed2026

Single-cell profiling reveals a novel CAF subpopulation linking stromal heterogeneity to immune suppression in breast cancer subtypes.

BACKGROUND: The tumor microenvironment critically influences breast cancer (BC) progression, immune surveillance, and therapeutic response. Cancer-associated fibroblasts (CAFs), a heterogeneous stromal population, are key regulators of these processes, yet their subtype-specific contributions in BC remain insufficiently defined. METHODS: We integrated three single-cell RNA sequencing datasets from 29 BC patients to characterize stromal populations. Bulk RNA-seq data from The Cancer Genome Atlas (TCGA) were analyzed to assess correlations between CAF subsets and immune infiltration. Gene signatures were derived to identify subtype-specific CAF-immune interactions, prognostic markers, and potential predictors of chemotherapy response. RESULTS: Three conserved stromal populations (iCAFs, myCAFs, and pericytes) were identified, along with a previously unrecognized subset, the cluster 3 (CL3) CAF-like cells, referred as metabolic stressed CAF (msCAF). msCAF cells displayed transcriptional programs associated with antigen presentation, stress response, glycolysis, and extracellular matrix remodeling. Their abundance was inversely correlated with T-cell infiltration and function, in a subtype-specific manner: triple negative breast cancer (TNBC) was enriched for msCAFs in immune-infiltrated but functionally constrained microenvironments, whereas Luminal A tumors exhibited weaker immune infiltration with heterogeneous CAF-immune associations. msCAFs were characterized by a conserved gene signature (HLA-A, HLA-C, IL32, EMP3) and subtype-specific genes related to T-cell exhaustion. Several genes demonstrated prognostic relevance with distinct patterns in Luminal A (IER3, TIMP1, TBX3, SEC61G) and TNBC (ADM, C4orf3, LDHA) tumors, as well as shared biomarkers (FN1, LOXL2, P4HA1). Multiple msCAF genes also predicted chemotherapy response, suggesting utility as treatment stratification biomarkers. CONCLUSION: msCAFs represent a clinically relevant CAF subset that drives immune suppression, impacts subtype-specific prognosis, and influences therapy response in BC. These findings highlight msCAFs as promising targets for enhancing immunotherapy and personalizing treatment strategies.

Humans↗
PubMed1979

Small cell carcinoma of the lung. Prognosis in relation to histologic subtype.

Forty-six of 59 patients with small cell carcinoma of the lung who were treated with multiple drug chemotherapy and radiotherapy were subclassified according to the World Health Organization classification. Subtyping was not possible in the 13 other patients who were diagnosed on sputum cytology findings alone. There was no significant difference in extent of disease, response, duration of response to treatment, or median survival between the different subtypes. Two main difficulties arise in applying the subtyping classification. First, many tumors showed features of several histologic subtypes, implying the existence of a morphologic continuum within the general group of small cell anaplastic carcinomas. Second, tissue crushing artefact was common. Our results do not reveal any advantage in knowing the tumor subtype. It remains essential to differentiate small cell anaplastic carcinoma from other forms of lung carcinoma not responsive to chemotherapy.

Antineoplastic Agents↗